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Record W2410514806

Evaluating the Spacing Effect Theory on the Instructional Effectiveness of Semester-Length versus Quarter-Length Introductory Computer Literacy Courses in Institutions of Higher Learning

2013· article· en· W2410514806 on OpenAlexaboutno aff
Emelda S. Ntinglet

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationQuarter (Canadian coin)Computer scienceLiteracyPedagogyMathematicsPsychology
DOInot available

Abstract

fetched live from OpenAlex

Evaluating the Spacing Effect Theory on the Instructional Effectiveness of Semester-Length versus Quarter-Length Introductory Computer Literacy Courses in Institutions of Higher Learning. Emelda S. Ntinglet-Davis 2013: Applied Dissertation, Nova Southeastern University, Abraham S. Fischler School of Education and Human Services. ERIC Descriptors: Community College, Spacing effect, Retention, Scheduling, Education, Instructional effectiveness, Intensive format, Quarter-length format, Semester format. This mixed research study evaluated the spacing effect theory on the academic performances of students enrolled in introductory level Computer Literacy courses by comparing course grades and mock IC3 certification exam scores in semester-length and quarter-length courses at Prince Georges Community College. The study was ingrained on the spacing effect theory which posits that mammals will tend to recall material learned over time (spaced presentation) than material concepts learned over shorter periods (massed presentation). A t test analysis revealed that students in the quarter-length formats had significantly higher grades than those in the semester format but presented no significant difference on their mock IC3 scores. A Pearson correlation conducted also revealed no significant relationship among students' course grades and their mock IC3 scores overall or by format (semester vs. intensive).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.336
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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